{"id":"W6920616493","doi":"10.6068/dp14ba8bb354b52","title":"Trend 1974 - 2006. Statistics Canada. CANSIM: Income, Pensions, Spending and Wealth - Pensions Plans and Funds and Other Retirement Income Programs | Country: Canada | Table: Registered pension plans (RPPs) and members, by class of employees eligible for the plan, sector, type of plan and contributory status | Variable: All employees, Plans, Defined benefit registered pension plans | Units: # %, 1974-2006. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-122.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pension; Descriptive statistics; Census; Social security; Socioeconomic status; Population; Publication; Official statistics; Personal income; Summary statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001643095,0.002354704,0.002694574,0.008344755,0.002754718,0.004360431,0.004504252,0.001343466,0.07374117],"category_scores_gemma":[0.01578889,0.001626043,0.001806341,0.04037872,0.0006043527,0.002118992,0.001982185,0.002809623,0.05354353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0421472,"about_ca_system_score_gemma":0.09697233,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9910306,"about_ca_topic_score_gemma":0.9902109,"domain_scores_codex":[0.9965571,0.000215468,0.0003982223,0.0005326667,0.001540059,0.0007564397],"domain_scores_gemma":[0.9732354,0.001153066,0.001022103,0.0009719642,0.02234513,0.001272403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002108114,0.000005699966,0.0008619503,0.0002061234,0.00001967765,0.000005476097,0.00001652484,0.00009804945,0.000008796747,0.0002891965,0.9973277,0.001139662],"study_design_scores_gemma":[0.0001470543,0.000009110809,0.01862746,0.0006467528,0.0000580419,0.00002126952,0.0003255963,0.0003810963,0.0001653725,0.0005271544,0.9790273,0.00006377891],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003671801,0.00003410195,0.00001232862,0.00005926819,0.00001433756,0.000007109725,0.9993192,0.00003988847,0.0004771002],"genre_scores_gemma":[0.0004241405,0.0001386504,0.0001750231,0.00006088763,0.000009219531,0.00005622068,0.997137,0.00005024756,0.00194878],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07374117,"threshold_uncertainty_score":0.3058008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0717893355598911,"score_gpt":0.290275696359484,"score_spread":0.2184863607995929,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}